Realization and discussion of selected artificial intelligence algorithms in computer games

The study explores the usage of reinforcement learning algorithms in computer card games, such as Proximal Policy Optimization and Monte Carlo Tree Search. The aim is to evaluate the efficiency and learning ability across different scenarios, such as Blackjack and Poker Limit Hold'em. Comparative analysis focuses on key metrics: learning speed, stability, reward evaluation and win rate. The results highlight strengths and limitations of PPO and MCTS. Also, the potential of hybrid approaches is discussed, that combine the strategic depth of MCTS with PPO's computational efficiency to create versatile AI agents capable of excelling in diverse gaming environments. The findings underscore the importance of aligning algorithmic characteristics with task specifics and domain factors.